Search for collections on Wintec Research Archive

Data Mining Techniques to Diagnose Diabetes Using Blood Lipids


Full text not available from this repository. (Request a copy)


Introduction: Nowadays, diabetic disease is one of the most common, dangerous and costly diseases in the world spreading rapidly. Data mining techniques can be used for early diagnosis of this disease which results in preventing a lot of problems for patients including heart diseases, vision problems and kidney disorders.
Matherials & methods: In this research, the Rapid Miner software has been used as a modeling tool to classify each patient as either diabetic or non-diabetic. The data set of this research has been collected from the database of one lab in Nahavand which includes the information of 5706 patients in a five years period from 2009 to 2013. The data set includes such information about patients as: age, gender, the level of lipid in the blood and the amount of fasting blood sugar.
Findings: After modeling with different classification techniques, the best accuracy achieved from the decision tree c4.5 which was 90.02%.
Discussion & Conclusion: For early diagnosis of diabetes in many countries around the world many techniques have been proposed using a variety of methods and variables. In the current research, using the relationship between blood lipids and fasting blood sugar, a method based on data mining techniques for diagnosing diabetes has been proposed.

Item Type: Journal article
Uncontrolled Keywords: Data Mining, Diabetes, Classification techniques, Decision tree C4.5.
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: Schools > Centre for Business, Information Technology and Enterprise > School of Information Technology
Depositing User: Reza Rafeh
Date Deposited: 10 Jan 2017 20:32
Last Modified: 21 Jul 2023 04:34

Actions (login required)

View Item
View Item